
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow

class __snake_case						:
    def  _SCREAMING_SNAKE_CASE			(    self						,snake_case						,snake_case						,snake_case ):



           '''simple docstring'''







           return None






class __snake_case						:
    def  _SCREAMING_SNAKE_CASE			(    self						,snake_case						,snake_case						,snake_case						,snake_case ):



           '''simple docstring'''







           return None






class __snake_case						( unittest.TestCase	):
    _a						:		str=							[
        # (model_name, model_kwargs)
        ("bert-base-cased", {}),
        ("gpt2", {"use_cache": False}),  # We don't support exporting GPT2 past keys anymore
    ]
    @require_tf
    @slow
    def  _SCREAMING_SNAKE_CASE			(    self ):



           '''simple docstring'''







           for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                  self._test_export(_A						,"""tf"""						,12						,**_A )
    @require_torch
    @slow
    def  _SCREAMING_SNAKE_CASE			(    self ):



           '''simple docstring'''







           for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                  self._test_export(_A						,"""pt"""						,12						,**_A )
    @require_torch
    @slow
    def  _SCREAMING_SNAKE_CASE			(    self ):



           '''simple docstring'''







           from transformers import BertModel

           lowercase				:	Optional[Any]			      = ['''[UNK]''', '''[SEP]''', '''[CLS]''', '''[PAD]''', '''[MASK]''', '''some''', '''other''', '''words''']
           with NamedTemporaryFile(mode="""w+t""" ) as vocab_file:
                  vocab_file.write("""\n""".join(_A ) )
                  vocab_file.flush()
                  lowercase				:	int			      = BertTokenizerFast(vocab_file.name )

           with TemporaryDirectory() as bert_save_dir:
                  lowercase				:	int			      = BertModel(BertConfig(vocab_size=len(_A ) ) )
                  model.save_pretrained(_A )
                  self._test_export(_A						,"""pt"""						,12						,_A )
    @require_tf
    @slow
    def  _SCREAMING_SNAKE_CASE			(    self ):



           '''simple docstring'''







           for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                  lowercase				:	str			      = self._test_export(_A						,"""tf"""						,12						,**_A )
                  lowercase				:	Optional[Any]			      = quantize(Path(_A ) )

                  # Ensure the actual quantized model is not bigger than the original one
                  if quantized_path.stat().st_size >= Path(_A ).stat().st_size:
                         self.fail("""Quantized model is bigger than initial ONNX model""" )
    @require_torch
    @slow
    def  _SCREAMING_SNAKE_CASE			(    self ):



           '''simple docstring'''







           for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                  lowercase				:	Dict			      = self._test_export(_A						,"""pt"""						,12						,**_A )
                  lowercase				:	Tuple			      = quantize(_A )

                  # Ensure the actual quantized model is not bigger than the original one
                  if quantized_path.stat().st_size >= Path(_A ).stat().st_size:
                         self.fail("""Quantized model is bigger than initial ONNX model""" )
    def  _SCREAMING_SNAKE_CASE			(    self						,snake_case						,snake_case						,snake_case						,snake_case=None						,**snake_case ):



           '''simple docstring'''







           try:
                  # Compute path
                  with TemporaryDirectory() as tempdir:
                         lowercase				:	Optional[int]			      = Path(_A ).joinpath("""model.onnx""" )

                  # Remove folder if exists
                  if path.parent.exists():
                         path.parent.rmdir()

                  # Export
                  convert(_A						,_A						,_A						,_A						,_A						,**_A )

                  return path
           except Exception as e:
                  self.fail(_A )
    @require_torch
    @require_tokenizers
    @slow
    def  _SCREAMING_SNAKE_CASE			(    self ):



           '''simple docstring'''







           from transformers import BertModel

           lowercase				:	Tuple			      = BertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random""" ) )
           lowercase				:	Any			      = BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random""" )
           self._test_infer_dynamic_axis(_A						,_A						,"""pt""" )
    @require_tf
    @require_tokenizers
    @slow
    def  _SCREAMING_SNAKE_CASE			(    self ):



           '''simple docstring'''







           from transformers import TFBertModel

           lowercase				:	Optional[int]			      = TFBertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random""" ) )
           lowercase				:	Tuple			      = BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random""" )
           self._test_infer_dynamic_axis(_A						,_A						,"""tf""" )
    def  _SCREAMING_SNAKE_CASE			(    self						,snake_case						,snake_case						,snake_case ):



           '''simple docstring'''







           lowercase				:	Tuple			      = FeatureExtractionPipeline(_A						,_A )

           lowercase				:	Optional[Any]			      = ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''output_0''', '''output_1''']
           lowercase				:	Optional[Any]			      = infer_shapes(_A						,_A )

           # Assert all variables are present
           self.assertEqual(len(_A )						,len(_A ) )
           self.assertTrue(all(var_name in shapes for var_name in variable_names ) )
           self.assertSequenceEqual(variable_names[:3]						,_A )
           self.assertSequenceEqual(variable_names[3:]						,_A )

           # Assert inputs are {0: batch, 1: sequence}
           for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
                  self.assertDictEqual(shapes[var_name]						,{0: """batch""", 1: """sequence"""} )

           # Assert outputs are {0: batch, 1: sequence} and {0: batch}
           self.assertDictEqual(shapes["""output_0"""]						,{0: """batch""", 1: """sequence"""} )
           self.assertDictEqual(shapes["""output_1"""]						,{0: """batch"""} )
    def  _SCREAMING_SNAKE_CASE			(    self ):



           '''simple docstring'''







           lowercase				:	Any			      = ['''input_ids''', '''attention_mask''', '''token_type_ids''']
           lowercase				:	Union[str, Any]			      = {'''input_ids''': [1, 2, 3, 4], '''attention_mask''': [0, 0, 0, 0], '''token_type_ids''': [1, 1, 1, 1]}
           lowercase				:	List[Any]			      = ensure_valid_input(FuncContiguousArgs()						,_A						,_A )

           # Should have exactly the same number of args (all are valid)
           self.assertEqual(len(_A )						,3 )

           # Should have exactly the same input names
           self.assertEqual(set(_A )						,set(_A ) )

           # Parameter should be reordered according to their respective place in the function:
           # (input_ids, token_type_ids, attention_mask)
           self.assertEqual(_A						,(tokens["""input_ids"""], tokens["""token_type_ids"""], tokens["""attention_mask"""]) )

           # Generated args are interleaved with another args (for instance parameter "past" in GPT2)
           lowercase				:	List[Any]			      = ensure_valid_input(FuncNonContiguousArgs()						,_A						,_A )

           # Should have exactly the one arg (all before the one not provided "some_other_args")
           self.assertEqual(len(_A )						,1 )
           self.assertEqual(len(_A )						,1 )

           # Should have only "input_ids"
           self.assertEqual(inputs_args[0]						,tokens["""input_ids"""] )
           self.assertEqual(ordered_input_names[0]						,"""input_ids""" )
    def  _SCREAMING_SNAKE_CASE			(    self ):



           '''simple docstring'''







           lowercase				:	Union[str, Any]			      = generate_identified_filename(Path("""/home/something/my_fake_model.onnx""" )						,"""-test""" )
           self.assertEqual("""/home/something/my_fake_model-test.onnx"""						,generated.as_posix() )








'''simple docstring'''





import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase						:						Optional[Any]  =					logging.get_logger(__name__)

_UpperCamelCase						:						Dict  =					{
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}


class  snake_case__      (					UpperCamelCase):
					a_        =					"mvp"
					a_        =					["past_key_values"]
					a_        =					{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




					def __init__(  self  :		Any				, _A  :		List[str]=5_02_67				, _A  :		Dict=10_24				, _A  :		List[str]=12				, _A  :		Dict=40_96				, _A  :		Optional[int]=16				, _A  :		Optional[Any]=12				, _A  :		int=40_96				, _A  :		Dict=16				, _A  :		Union[str, Any]=0.0				, _A  :		Optional[Any]=0.0				, _A  :		int="gelu"				, _A  :		Union[str, Any]=10_24				, _A  :		int=0.1				, _A  :		Optional[Any]=0.0				, _A  :		Optional[Any]=0.0				, _A  :		Optional[Any]=0.02				, _A  :		Optional[Any]=0.0				, _A  :		str=False				, _A  :		List[Any]=True				, _A  :		Optional[int]=1				, _A  :		Tuple=0				, _A  :		Dict=2				, _A  :		Dict=True				, _A  :		int=2				, _A  :		Tuple=2				, _A  :		List[str]=False				, _A  :		Any=1_00				, _A  :		Union[str, Any]=8_00				, **_A  :		List[str]				, )    ->							int:
								UpperCAmelCase_      :       Union[str, Any]          =       vocab_size
								UpperCAmelCase_      :       int          =       max_position_embeddings
								UpperCAmelCase_      :       Dict          =       d_model
								UpperCAmelCase_      :       Dict          =       encoder_ffn_dim
								UpperCAmelCase_      :       Optional[Any]          =       encoder_layers
								UpperCAmelCase_      :       Tuple          =       encoder_attention_heads
								UpperCAmelCase_      :       List[Any]          =       decoder_ffn_dim
								UpperCAmelCase_      :       List[Any]          =       decoder_layers
								UpperCAmelCase_      :       Dict          =       decoder_attention_heads
								UpperCAmelCase_      :       str          =       dropout
								UpperCAmelCase_      :       Optional[Any]          =       attention_dropout
								UpperCAmelCase_      :       Optional[Any]          =       activation_dropout
								UpperCAmelCase_      :       Union[str, Any]          =       activation_function
								UpperCAmelCase_      :       Union[str, Any]          =       init_std
								UpperCAmelCase_      :       Optional[int]          =       encoder_layerdrop
								UpperCAmelCase_      :       Optional[int]          =       decoder_layerdrop
								UpperCAmelCase_      :       Dict          =       classifier_dropout
								UpperCAmelCase_      :       List[Any]          =       use_cache
								UpperCAmelCase_      :       Dict          =       encoder_layers
								UpperCAmelCase_      :       Tuple          =       scale_embedding  # scale factor will be sqrt(d_model) if True
								UpperCAmelCase_      :       int          =       use_prompt
								UpperCAmelCase_      :       Tuple          =       prompt_length
								UpperCAmelCase_      :       str          =       prompt_mid_dim

								super().__init__(
								    pad_token_id=_A				, bos_token_id=_A				, eos_token_id=_A				, is_encoder_decoder=_A				, decoder_start_token_id=_A				, forced_eos_token_id=_A				, **_A				, )

								if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''				, _A      ):
											UpperCAmelCase_      :       Optional[Any]          =       self.bos_token_id
											warnings.warn(
											    F"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
											    '''The config can simply be saved and uploaded again to be fixed.'''      )


